Instructions to use H2Ozone/dorm_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use H2Ozone/dorm_training with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=H2Ozone/dorm_training \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=H2Ozone/dorm_training - Notebooks
- Google Colab
- Kaggle
File size: 1,897 Bytes
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"name": "policy_preprocessor",
"steps": [
{
"registry_name": "rename_observations_processor",
"config": {
"rename_map": {}
}
},
{
"registry_name": "to_batch_processor",
"config": {}
},
{
"registry_name": "smolvla_new_line_processor",
"config": {}
},
{
"registry_name": "tokenizer_processor",
"config": {
"max_length": 48,
"task_key": "task",
"padding_side": "right",
"padding": "max_length",
"truncation": true,
"tokenizer_name": "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
}
},
{
"registry_name": "device_processor",
"config": {
"device": "cuda",
"float_dtype": null
}
},
{
"registry_name": "normalizer_processor",
"config": {
"eps": 1e-08,
"features": {
"observation.state": {
"type": "STATE",
"shape": [
6
]
},
"observation.images.camera1": {
"type": "VISUAL",
"shape": [
3,
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256
]
},
"observation.images.camera2": {
"type": "VISUAL",
"shape": [
3,
256,
256
]
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"observation.images.camera3": {
"type": "VISUAL",
"shape": [
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},
"action": {
"type": "ACTION",
"shape": [
6
]
}
},
"norm_map": {
"VISUAL": "IDENTITY",
"STATE": "MEAN_STD",
"ACTION": "MEAN_STD"
}
},
"state_file": "policy_preprocessor_step_5_normalizer_processor.safetensors"
}
]
} |